Skip to main content

Construction of Low-Carbon Economic Model Based on Grey Clustering Algorithm and Big Data

  • Conference paper
  • First Online:
Recent Advancements in Computational Finance and Business Analytics (CFBA 2023)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 32))

  • 206 Accesses

Abstract

Due to the influence of global climate, energy, economy and other factors, low-carbon economy has won the consensus of all countries, has risen to the height of national and regional development strategies. At present, China's low-carbon research is still in its infancy, so we should grasp its research status as a whole, so as to make a better in-depth study. Therefore, this paper further analyzes the low-carbon economy based on grey clustering algorithm and big data, and builds a model in this paper. In this paper, the low-carbon economy is analyzed based on the grey clustering algorithm and big data. Simulation results show that the algorithm in this paper has the highest accuracy rate, with the accuracy rate of 88.72%, followed by the GA (genetic algorithm) algorithm with the accuracy rate of 65.74%. Finally, the machine learning algorithm, the accuracy rate is 44.76%. Through the grey clustering algorithm, the concept of low carbon is established, the concept innovation is realized, the idea that “low carbon economy is an important way to implement scientific outlook on development” is earnestly implemented, and the concept, connotation and measures of low carbon economy and the importance and necessity of developing low carbon economy are widely publicized.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. H. Yin, J. Zhao, X. Xi, et al., Evolution of regional low-carbon innovation systems with sustainable development: An empirical study with big-data. J. Clean. Prod. 209(1), 1545–1563 (2019)

    Google Scholar 

  2. F. Li, R. Li, Z. Zhang et al., Big data analytics for flexible energy sharing: Accelerating a low-carbon future. IEEE Power Energ. Mag. 16(3), 35–42 (2018)

    Article  Google Scholar 

  3. L. Xin, Low carbon economic competitiveness statistic measure analysis under big data perspective. Basic Clin. Pharmacol. Toxicol. 28(S1), 124–137 (2019)

    Google Scholar 

  4. Z. Zhang, J. Li, Big-data-driven low-carbon management—ScienceDirect. Big Data Min. Clim. Chang. 37(19), 287–299 (2020)

    Article  Google Scholar 

  5. D. Ma, J. Hu, F. Yao, Big data empowering low-carbon smart tourism study on low-carbon tourism O2O supply chain considering consumer behaviors and corporate altruistic preferences—ScienceDirect. Comput. Ind. Eng. 52(20), 19–37 (2020)

    Google Scholar 

  6. A. Singh, S. Kumari, H. Malekpoor, et al., Big data cloud computing framework for low carbon supplier selection in the beef supply chain. J. Clean. Prod. 202(20), 139–149 (2018)

    Google Scholar 

  7. P.H. Lyu, E. Ngai, P.Y. Wu, Scientific data-driven evaluation on academic articles of low-carbon economy. Energy Policy 125(30), 358–367 (2019)

    Article  Google Scholar 

  8. D. Zhu, Evaluation of insulation state based on improved grey clustering algorithm. Peak Data Sci. 28(16), 28–49 (2017)

    Google Scholar 

  9. X.I.E. Naiming, S.U. Bentao, C.H.E.N. Nanlei, Construction mechanism of whitenization weight function and its application in grey clustering evaluation. Syst. Eng. Electron. Technol.: Engl. 30(1) 11–22 (2019)

    Google Scholar 

  10. N. Xie, S.U. Bentao, N. Chen, Construction mechanism of whitenization weight function and its application in grey clustering evaluation. J. Syst. Eng. Electron. 30(01), 125–135 (2019)

    Google Scholar 

  11. L.T. Ding, J.L. Fan, Y.X. Liu, Method of safety evaluation for system group based on grey clustering. Comput. Sci. 26(10), 11–19 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yongqiang Lin .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lin, Y. (2023). Construction of Low-Carbon Economic Model Based on Grey Clustering Algorithm and Big Data. In: Gupta, R., Bartolucci, F., Katsikis, V.N., Patnaik, S. (eds) Recent Advancements in Computational Finance and Business Analytics. CFBA 2023. Learning and Analytics in Intelligent Systems, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-38074-7_18

Download citation

Publish with us

Policies and ethics